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FAIR principles for AI models with a practical application for accelerated high energy diffraction microscopy.

Nikil Ravi1,2,3, Pranshu Chaturvedi1,2, E A Huerta4,5

  • 1Data Science and Learning Division, Argonne National Laboratory, Lemont, Illinois, 60439, USA.

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|November 10, 2022
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Summary
This summary is machine-generated.

New FAIR principles for artificial intelligence (AI) models promote data sharing and reuse. This framework enables autonomous AI-driven scientific discovery using advanced computing resources.

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Area of Science:

  • Scientific data management
  • Artificial intelligence in science

Background:

  • The Findable, Accessible, Interoperable and Reusable (FAIR) principles have advanced scientific data management.
  • Artificial intelligence (AI) is increasingly impacting scientific research and engineering.

Purpose of the Study:

  • To introduce practical, measurable FAIR principles specifically for AI models.
  • To demonstrate a unified computational framework for managing FAIR data and AI models.

Main Methods:

  • Leveraging the Advanced Photon Source, Materials Data Facility, Data and Learning Hub for Science, and funcX.
  • Utilizing the Argonne Leadership Computing Facility (ALCF), including ThetaGPU and SambaNova DataScale® systems.
  • Developing a domain-agnostic computational framework.

Main Results:

  • Establishment of a unified framework for FAIR data and AI model management.
  • Demonstration of a system integrating experimental facilities, data resources, and high-performance computing.
  • A pathway towards enabling autonomous AI-driven discovery.

Conclusions:

  • The proposed FAIR principles for AI models are practical and measurable.
  • The unified computational framework facilitates the creation and sharing of FAIR data and AI models.
  • This approach supports autonomous AI-driven discovery across scientific domains.